In this paper, we introduce an image quality assessment (IQA) method for pediatric T1- and T2-weighted MR images. IQA is first performed slice-wise using a nonlocal residual neural network (NR-Net) and then volume-wise by agglomerating the slice QA results using random forest. Our method requires only a small amount of quality-annotated images for training and is designed to be robust to annotation noise that might occur due to rater errors and the inevitable mix of good and bad slices in an image volume. Using a small set of quality-assessed images, we pre-train NR-Net to annotate each image slice with an initial quality rating (i.e., pass, questionable, fail), which we then refine by semi-supervised learning and iterative self-training. Experimental results demonstrate that our method, trained using only samples of modest size, exhibit great generalizability, capable of real-time (milliseconds per volume) large-scale IQA with near-perfect accuracy.

中文翻译：

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通过半监督深度非局部残差神经网络对儿科 MRI 进行实时质量评估。

在本文中，我们介绍了一种用于儿科 T1 和 T2 加权 MR 图像的图像质量评估 (IQA) 方法。IQA 首先使用非局部残差神经网络 (NR-Net) 逐片执行，然后通过使用随机森林聚合切片 QA 结果逐片执行。我们的方法只需要少量带质量注释的图像进行训练，并且设计为对可能由于评分者错误和图像体积中不可避免的好坏切片混合而产生的注释噪声具有鲁棒性。使用一小组质量评估图像，我们预训练 NR-Net 以使用初始质量评级（即通过、有问题、失败）注释每个图像切片，然后我们通过半监督学习和迭代自我改进训练。实验结果表明，我们的方法仅使用中等大小的样本进行训练，